Coherent Interference Detection

ABSTRACT

A method for detecting coherent interference includes the steps of: (a) receiving a signal at a first frequency, (b) forming a set of cross-correlation results by at least cross-correlating the signal with a first known code for a plurality of code offsets, (c) determining a statistical signature of the cross-correlation results, and (d) deciding, based on the statistical signature, whether non-negligible coherent interference is present within a search bin defined by the combination of the first frequency and the first known code.

BACKGROUND OF THE INVENTION

The present invention relates to a method and system for detectinginterference.

Interference is a problem in many wireless signalling systems. Examplesinclude Global Navigation Satellite Systems (GNSS). GNSS use satellitesfor geo-spatial positioning with global coverage. The best known exampleof these systems is the Global Positioning System, or GPS. Using thesesystems small electronic receivers are able to determine their location(longitude, latitude, and altitude) to within a few meters using thetravel times of signals transmitted along a line-of-sight by radio frommultiple satellites.

GPS signals are formed of a navigation message binary phase shiftmodulated (BPSK) onto a direct sequence spread spectrum (DSSS) signal.The spread spectrum signal comprises a unique pseudo-noise (PN) codethat identifies the satellite. For civil application GPS signalstransmitted using the L1 carrier frequency, this code is known as theC/A code. The C/A code is a member of a Gold code family. Other codefamilies are in use for other global navigation satellite systems. TheC/A code has a sequence length of 1023 chips and it is spread with a1.023 MHz chipping rate. The code sequence therefore repeats everymillisecond. The code sequence has an identified start instant when thetwo code generators in the satellite just transition to the all state.This instant is known as the code epoch. After various transport delaysin the satellite, the code epoch is broadcast through the timing andsequence of specific code states assigned to the satellite. Thissignalling event can be recognised, in suitably adapted receivers,through a process of aligning a replica code with the code received fromeach satellite.

The navigation message has a data rate of 50 bits per second, lower thanthe code rate, and its data bit or symbol transitions are synchronisedwith the start of the C/A code sequence. Each bit of the navigationmessage lasts for 20 milliseconds and thus incorporates 20 repetitionsof the CIA code. The navigation message is constructed from a 1500-bitframe consisting of five 300-bit sub-frames. Each sub-frame lasts for 6seconds. The satellite transmits the navigation message and C/A codeusing a carrier frequency that is an integer multiple of 10.23 MHz (forthe L1 carrier, the multiple is 154).

As mentioned above, a GPS receiver may determine the time-of-arrival ofa signalling event through a process of aligning a replica code with thecode received from each satellite. The receiver may also use TOW (timeof week) information contained in the navigation message to determinethe time when the signalling event was transmitted. From this, thereceiver can determine the transit time for the signalling event (fromwhich it can determine the distance between it and the satellite),together with the position of the satellite at the time when thesignalling event was transmitted (using ephemeris information sent withthe navigation signal). The receiver can then calculate its ownposition. Theoretically, the position of the GPS receiver can bedetermined using signals from three satellites, providing the receiverhas a precise time reference or knowledge of part of the positions, suchas altitude. However, in practice GPS receivers use signals from four ormore satellites to determine an accurate three-dimensional locationsolution because an offset between the receiver clock and GPS timeintroduces an additional unknown into the calculation.

The first stage of processing during which the visible signals areidentified is known as signal acquisition. To find the signals from anysatellites that may be in view the receiver may search over a range ofpossible time and frequency offsets. Although GPS is a spread spectrumsystem and therefore all the satellites broadcast the C/A code onnominally the same frequency, a frequency search is still required ifthere is any uncertainty in the frequency of the receiver's local clockor if there is any uncertainty in the receiver's knowledge of therelative motion of the receiver and the satellites. In order to find oneparticular satellite with a PN known to the receiver, the receiver mustcompare the signal it receives to the expected signal, usually using across-correlation method. If the receivers time uncertainty exceeds thecode duration then the code transmission can be at any point in itscycle, and the receiver must search over all possible code phases. Inthis specification a search for a particular PN at a particularfrequency over all potential code phases is referred to as a searchspanning a particular search bin.

These signal searches can mistakenly identify spurious signals caused bycoherent interferers. These may be due to cross-correlation from othersatellites of the same constellation, augmentation satellites, otherconstellations of satellites, narrowband interferers or local jammers,amongst others. Such spurious signals can result in process resources atthe receiver being overwhelmed and invalid signals being identified.Interferers are commonly PN- and frequency-specific but tend to impactmany code phases within a search bin.

In a system of the type described above, coherent interferers havewaveforms that include repetitive characteristics, the periodicityrelated to the CA code repeat period. In the presence of a coherentinterferer the matched filter results for a particular code phase mayhave a bias that can persist for a period of time. This period of timedepends on the rate at which the interfering waveform drifts withrespect to the matched filter code phases. The coherentinterferer-induced bias creates a problem for GPS receivers because itaccumulates in exactly the same way as a real GPS signal. Thus even asmall coherent interferer may look to the detection logic like a realsignal or it may obscure the real signal.

The conventional approach to eliminating coherent interferers is todetect strong satellites first and measure their signal strengths andfrequencies. A look-up table is then used to determine if a weakercandidate satellite signal could be a further cross-correlation from oneof the strong satellites. More advanced systems also consider thepossibility that the weak signal is due to interference from acombination of strong satellites. Additionally the interference dependson other factors that may or may not be known such as the coarsealignment of the receiver's local clock. These factors may beindividually taken into account, or a correction factor corresponding tothe overall worst case scenario could be applied.

An alternative technique is to determine the correct code phasealignment for each of the satellites from which strong signals are beingreceived and the magnitude of the received signals. Thecross-correlation values of these strong satellite signals with all theweaker ones still subject to search can then be determined. A process ofsubtraction may then be employed to remove the effects of thecross-correlation from all the strong satellite signals from thechannels used to search for the weaker satellite signals. This processcan significantly improve the detection sensitivity for the weakersatellite signals. However, it should be noted that this technique onlyworks well when the relative distances to each satellite are known towithin ½ chip, otherwise the cross-correlation subtraction does not takeplace in the correct respective search bin.

These existing methods require detailed knowledge of parameters relatingto the potential interferers including their identity, signal structureand signal strength. This parametric information is then used to workout the expected interference and discard signals from search bins whichare identified as potentially compromised. Therefore all possibleinterferers should be known and continuously monitored. This approachdoes not scale well in the presence of more than one interferer andtherefore, as more GNSS systems are deployed, this is an increasinglydemanding task. The emerging multiplicity of GNSS signal sources,especially in the L1 band, will undoubtedly increase the globalinterference suffered by GNSS receivers at least due to thecross-correlation effects between the various code families used. Thereis clearly practical difficulty in obtaining and maintaining a list ofall potential interferers. Additionally, complications arise whenmultiple coherent interferers are present. A general mechanism forcoherent interference rejection would be preferred over this parametricmechanism so that a receiver could be resilient to any present or futurecoherent interferer. What is needed is a signal acquisition method,implementable on relatively simple hardware, which can eliminatecoherent interference signals from signal acquisition without detailedparametric knowledge of interferers.

SUMMARY OF THE INVENTION

According to a first aspect of the invention, there is provided a methodfor detecting coherent interference, the method comprising the steps of:(a) receiving a signal at a first frequency, (b) forming a set ofcross-correlation results by at least cross-correlating the signal witha first known code for a plurality of code offsets, (c) determining afirst statistical signature of the cross-correlation results, and (d)deciding, based on the first statistical signature, whethernon-negligible coherent interference is present within a first searchbin defined by the combination of the first frequency and the firstknown code.

The said step (b) of forming the set of cross-correlation results maycomprise at least: (b.i) cross-correlating the signal with a first knowncode for a plurality of code offsets, and (b.ii) either (b.ii.1)determining the absolute values of the values output by the said step(b.i), or (b.ii.2) determining the squares of the values output by thesaid step (b.i).

The said step (b) of forming the set of cross-correlation results mayfurther comprise at least: (b.iii) non-coherently summing all the valuesdetermined in the said step (b.ii) each time a new value is determinedin the said step (b.ii).

The first known code may be a pseudorandom code.

The first statistical signature may indicate the extent to which peakvalues among the cross-correlation results are clustered about pluralcode offsets.

The method may comprise the step of: comparing the values determined inthe said step (b.ii.1) to a threshold value, and determining any codeoffsets having cross-correlation results higher than that thresholdvalue to be candidates for future receiving operations.

The method may comprise the step of calculating the threshold value asthe mean of the values determined in the said step (b.ii.1) plus amultiple of their standard deviation.

The method may comprise re-calculating the threshold value from time totime.

The method may comprise assessing whether the number of candidatesexceeds a predetermined value, and if it does determining there to benon-negligible coherent interference present in the first search bin.

The said step (d) may comprise comparing the first statistical signaturewith a first expected statistical signature.

The first expected statistical signature may be the statisticalsignature expected in the absence of non-negligible coherentinterference.

The first expected statistical signature may be that of additive whiteGaussian noise.

The coherent interference may be continuous wave interference and thefirst expected statistical signature may be that of a noise channel.

The said step (d) may comprise treating the received signal as having aconstant variance.

The method may comprise assessing whether the first expected andmeasured statistical signatures deviate by less than a firstpredetermined amount and if they do then determining that there isnegligible coherent interference in the first search bin.

The method may comprise assessing whether the first expected andmeasured statistical signatures deviate by more than a secondpredetermined amount and if they do then determining that there isnon-negligible coherent interference present in the first search bin.

The method may comprise assessing whether the first expected andmeasured statistical signatures deviate by an amount between said firstand second predetermined amounts, and if they do then in the said step(d) repeating the previous steps with an increased threshold value.

The method may comprise re-calculating the first and/or second amountsfrom time to time.

The first statistical signature may be the standard deviation of thecross-correlation results.

The method may be performed for a plurality of search bins.

The known code may be a code transmitted by a particular remotetransmitter.

The method may be performed as part of a signal acquisition process.

The received signal may originate from a satellite.

The method may further comprise the steps of: if the said step (d)resulted in a decision that coherent interference was present within thefirst search bin then (e.i) repeating the previous steps for a secondsearch bin, different to the first search bin; or if the said step (d)resulted in a decision that coherent interference was not present withinthe first search bin then (e.ii) determining a second statisticalsignature of the cross-correlation results, and deciding, based on thesecond statistical signature, whether non-negligible coherentinterference is present within the first search bin.

According to a second aspect of the invention, there is provided asystem for detecting coherent interference, the system comprising: asignal receiver for receiving a signal at a first frequency, across-correlator for forming a set of cross-correlation results by atleast cross-correlating the received signal with a first known code fora plurality of code offsets, a processor for determining a firststatistical signature of the cross-correlation results, and a decisionunit for deciding whether non-negligible coherent interference ispresent within a first search bin defined by the combination of theparticular frequency and the first known code based on the statisticalsignature.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of the present invention will now be described by way of examplewith reference to the accompanying drawings. In the drawings:

FIG. 1 is a schematic of an example system;

FIG. 2 shows the table of values built up by the NCS integrator;

FIG. 3 a shows a selection of AGC profiles;

FIG. 3 b plots κ over the course of acquisition for the AGC profiles ofFIG. 3 a;

FIG. 4 shows a cumulative distribution of R;

FIGS. 5 a-1, 5 a-2, 5 b and 5 c show example cross-correlation datacompared to Gaussian data;

FIG. 6 a plots R for a contaminated input signal;

FIG. 6 b plots R for a different contaminated signal;

FIG. 7 illustrates the null hypothesis;

FIG. 8 illustrates quantisation noise drift;

FIG. 9 illustrates quantisation noise drift for a long integration time;

FIG. 10 shows an example threshold shaping scheme;

FIG. 11 plots R for a signal contaminated by CW interference;

FIG. 12 is a schematic of a signal acquisition and verification system;

FIG. 13 is a schematic of a similar system with a CW detector;

FIG. 14 shows how CW interference can be detected before signaldetection;

FIG. 15 plots a typical CW detector output;

FIG. 16 plots the probability of CW detection against CNO;

FIG. 17 shows how CW interference can be detected after signaldetection; and

FIG. 18 is a flowchart of a signal detection process with CW detection.

DETAILED DESCRIPTION OF THE INVENTION

The following description is presented to enable any person skilled inthe art to make and use the system, and is provided in the context of aparticular application. Various modifications to the disclosedembodiments will be readily apparent to those skilled in the art.

The general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present invention. Thus, the present invention is not intended tobe limited to the embodiments shown, but is to be accorded the widestscope consistent with the principles and features disclosed herein.

FIG. 1 is a schematic of an example system which may use the method ofthe forthcoming disclosure. A receiver 1 may be a radio receiverintended to pick up signals from satellites such as a satellite 2. Thereceiver 1 may be housed in a positioning device which uses rangingsignals received from several satellites to estimate its own position.In order to receive and interpret the ranging signals it must be able tolock-on to each satellite's transmitted signal.

In operation, the satellite 2 may transmit a specific PN code on aspecific frequency. This PN code is repeated cyclically everymillisecond but is BPSK by data every 20 ms as described previously. Thereceiver 1 may be trying to lock-on to the satellite 2 and may know thesatellite 2's particular PN code and the frequency on which it istransmitted, but not the phase of the PN code signal with respect to thereceiver 1's local clock.

An antenna 3 may receive a radio signal. The signal received may bemixed down to baseband where it forms a stream of complex IQ data. Theamplitude of this raw data may be controlled by an autogain control(AGC) section 4. The amplified IQ data may then be correlated against amatched filter 5 which compares the received signal with the known PNcode used by the satellite 2.

If f(t) is the time-varying stream of IQ data output by the AGC section4, and g(t) is the receiver's stored replica of the PN code signal forthe satellite 2 appropriately phased for the frequency bin underconsideration then the matched filter 5 may perform the followingcorrelation:

$\begin{matrix}{{{f(t)}^{*}{g(t)}} = {{\int_{0}^{T}{{{f^{*}(t)} \cdot {g\left( {t - \tau} \right)}}\ {t}}} = {h(\tau)}}} & (1)\end{matrix}$

where T is the period of the regularly repeating PN code signal and thecorrelation is performed from time t=0 to time t=T. τ is the code phaseused for a particular correlation sample and its value varies from zeroto T. N samples may be taken. In practice this integral is approximatedby a discrete summation and, for the C/A codes used in GPS, typicalnumbers of samples per millisecond code cycle period are N=1536, N=2048and N=4096.

The summation approximating the integral of equation (1) may take placein “real time”, that is the integration variable t is increased at thesame rate as the real timeline so that IQ data is processed at the samerate at which it is received, with a short delay. Processing may also bedone faster or slower than “real time” by caching the IQ data as itcomes in and processing it later. For example, in a hardware receiverthe processing may be faster than “real time” so that multiple searchbins may be checked while one section of data is received. In that casethe summation may be performed such that advancing one unit of t may beequivalent to advancing a tiny fraction of that unit on the IQ datareception timeline. Conversely, in a software receiver the processingmay be slower than “real time” as the data is stored and read as needed.

The “real time” example will now be used for ease of explanation,although this will not usually be the most appropriate approach forreal-life situations. A new integration (summation) is started toproduce a new sample value h(τ) once every TIN seconds for T seconds sothat at time t=2T the search bin will be fully populated with a set of Nsamples each corresponding to N different code phases with consecutivecode phases separated by Δτ=T/N.

Due to the periodic nature of the C/A code it is convenient tocoherently integrate for an integer multiple of the code period, T, asthe cross correlation can be performed by accumulating data fromconsecutive periods prior to correlation against the matched filter. Ifthe receiver is tuned to exactly the same frequency as that on which thesatellite transmits then the longer the coherent integration time themore accurate the results, since the effects of random noise will cancelout over time. However in reality the frequencies of the satellite andreceiver are unlikely to be perfectly matched so phase errors areintroduced for longer integration times. Once the first correlation runis complete another identical run is completed and so on until asufficient quantity of data has been gathered. Each correlation runyields a complex result and during acquisition only the absolute valueof this result is typically retained. These absolute values from eachrun are accumulated, a process known as non-coherent summation. Theparticular lengths used for the coherent and non-coherent integrationsdepend on the stage of acquisition and the uncertainties in thereceiver. For example when detecting weak satellites many hundreds ofnon-coherent integrations may be performed.

The data set resulting from each correlation run could be used to plotthe modulus of the function h(τ). If there are no coherent interfererspresent then this function would be expected to show a single peak at awell-defined value of τ which could be identified and used to calculatethe code phase offset of the signal and thus, in combination withknowledge of the satellite locations, the distance to the satellite.However, the presence of coherent interferers produces multiple peaks,leading to confusion as to which code phase offset is the correct onefor locking-on to satellite 2. In the prior art, contaminated searchbins are discarded by using detailed parametric data relating topotential interference sources to identify spurious peaks. The method ofthe present example does not need any additional parametric informationto determine whether the signals in a particular search bin are likelyto be contaminated by coherent interferers.

As mentioned above the output from the matched filter after eachcoherent integration is a set of complex samples which spans the periodof the code. Each sample within the set corresponds to a different valueof the code phase τ. The samples in each set are labelled from n=1 ton=N. The sample set from each consecutive correlation run is labelled bym ranging from m=1 to m-M. The absolute values (abs) of the samples maybe recorded. The absolute value of the n^(th) sample in the m^(th) setis |h_(m)(nΔτ)|=s_(m,n). These sample values are accumulated bynon-coherent summation (NCS) in an NCS integrator 6. (The absolutevalues must be taken prior to summation in order to avoid complex valuescancelling each other out.) When the matched filter 5 completes itsfirst correlation run, labelled m=1, its N samples are stored in the NCSintegrator 6. When the second correlation run (m=2) is complete the newN samples are added to the respective previous N samples and so on tothe final run (m=M) so that a running total is kept of the correlationresults for each phase offset. FIG. 2 shows a table of the values storedby the NCS integrator 6 and will be referred to in more detail later.

The results of the NCS process may be passed to a statistical analyser 7which can calculate some statistical function or functions from them andpass this statistical data to a decision unit 8. The decision unit 8 canuse this statistical data to identify whether or not the particularsearch bin (the set of code phases for the satellite 2's specific PNcode and frequency) is likely to be contaminated with coherentinterference and thereby to determine whether or not to lock-on to thesatellite 2.

The process described above may be repeated for the search binsassociated with other known satellites until a sufficient number ofsatellite signals have been accepted as clean and locked-on to for thepositioning device to be able to estimate its position. The receiver maysearch many (e.g. 100) different search bins in parallel.

Thus a method of non-parametrically detecting coherent interferers whichcause cross-correlations during signal acquisition is provided whichidentifies contaminated bins by the statistical signature produced bythe impact of coherent interferers on multiple code phases in a searchbin.

The method can comprise estimating the statistical signature which couldbe expected from the NCS results in the absence of coherent interferersand comparing this with the statistical signature of the empirical NCSresults. Deviations from this expected statistical behaviour may then betreated as indicative of the presence of coherent interferers. Sincecoherent interferers may be expected to affect multiple code phase binsat a given PN and frequency (search bin), statistics can be calculatedon a per search bin basis.

Any method which continuously monitors the expected and actualstatistical properties of the non-coherent accumulation on a search binby search bin basis may be used to detect coherent interference.

For example, the expected standard deviation of the NCS integratoroutput in a search bin in the absence of coherent interferers may becalculated by measuring the mean of the NCS integrator output that wouldbe expected for a clean input and using a known relationship between themean and standard deviation for such an input. The actual standarddeviation of the empirical

NCS integrator output in the search bin may then be measured andcompared with the predicted value. If the search bin is free of coherentinterferers the ideal ratio of the two standard deviations is 1.

For a typical GPS receiver the GPS power density is below the thermalnoise floor, therefore in the absence of coherent interferers the inputto the matched filter looks like additive white Gaussian noise (AWGN).(The GPS signal is distinguishable over the thermal noise floor as aresult of processing gains introduced by the integrations.) For such anAWGN input it is possible to calculate expected statistical behaviour.Example methods for doing this are given below.

Example Methods for Predicting the Standard Deviation of NCS IntegratorOutput in a Search Bin in the Absence of Coherent Interference

The expected search bin mean and standard deviation which the realreceived signal is tested against, that is the statistical behaviour ofan AWGN input under the null hypothesis in which no coherent interferersare present, may be calculated as follows.

For an AWGN input after correlation and abs the absolute sample valuesare distributed as Rayleigh variates. The known relationship between themean of the abs values for correlation run m (μ_(m)) and their standarddeviation (σ_(m)) is:

$\begin{matrix}{\sigma_{m} = {\mu_{m}\sqrt{\frac{4 - \pi}{\pi}}}} & (2)\end{matrix}$

That is, the standard deviation of the abs values for correlation run m(σ_(m)), is equal to the mean of the abs values for that correlation run(μ_(m)) multiplied by the square root of: four minus pi, all divided bypi.

For convenience, the constant factor alpha equal to the square root of:four minus pi, all divided by pi is introduced:

$\begin{matrix}{\alpha = \sqrt{\frac{4 - \pi}{\pi}}} & (3)\end{matrix}$

This allows equation (2) to be rewritten as:

σ_(m)=αμ_(m)   (4)

That is, the standard deviation of the abs values for correlation run m(σ_(m)), is directly proportional to the mean of the abs values for thatcorrelation run (μ_(m)), with constant of proportionality alpha, definedby equation (3).

The mean for each correlation run can be written in terms of thestandard deviation of the incoming IQ data as follows:

$\begin{matrix}{\mu_{m} = {\sqrt{\frac{N\; \pi}{2}}\sigma_{iq}}} & (5)\end{matrix}$

That is, the mean of the abs values for a correlation run m (μ_(m)) isdirectly proportional to the standard deviation of the pre-correlationIQ data (σ_(iq)). The constant of proportionality is the square root of:the number of samples in each correlation run (N) multiplied by pi on 2.

After non-coherent summation of M correlation runs the mean of thesearch bin (μ) is given by the sum of all the correlation run means(μ_(m)) thus far:

$\begin{matrix}{\mu = {\sum\limits_{m = 1}^{M}\mu_{m}}} & (6)\end{matrix}$

As the samples are uncorrelated their variances add:

$\begin{matrix}{\sigma^{2} = {\sum\limits_{m = 1}^{M}\sigma_{m}^{2}}} & (7)\end{matrix}$

That is, the variance for the search bin under consideration afternon-coherent integration of M correlation runs (σ²) is equal to the sumover correlation runs labelled m=1 to m=M of each correlation runvariance (σ² _(m)).

Continuous Accumulation Method

Equations (6) and (7) may be applied directly to estimate the mean andvariance respectively as they accumulate. For example, if the mean forthe search bin under consideration after non-coherent integration of Mcorrelation runs (μ) is known through measurement then:

μ_(M)=μ−μ_(M−1)   (8)

That is, the mean for current correlation run M (μ_(M)) may becalculated by subtracting the mean for the previous correlation run(μ_(M−1)) from the mean for the search bin as measured by processing thecurrent correlation run (μ).

Similarly:

σ² _(M)=σ²−σ² _(M−1)   (9)

That is, the variance for current correlation run M (σ² _(M)) is equalto the variance for the search bin as found by processing the currentcorrelation run (σ²), minus the variance for the search bin found byprocessing the previous sample (σ² _(M−1)).

Thus, by rearranging equation (9) and substituting using equation (4),the variance for the search bin under consideration (σ²) may be obtainedas:

σ²=σ² _(M−1)+α²μ² _(M)   (10)

That is, the variance for the search bin under consideration as found byprocessing the current sample (σ²), is equal to the variance for theprevious correlation run (σ² _(M−1)) plus: alpha squared multiplied bythe square of the current correlation run mean (μ² _(M)).

Using equations (8) and (10) the value found for the expected variance(and hence its square root, the expected standard deviation) for thesearch bin may be updated after every sample.

Whether this continuous accumulation is useful or not depends on howrapidly the AGC changes during an integration. If the AGC is stronglytime-dependent then so is the input standard deviation of the IQ data(σ_(iq)) and the continuous accumulation method above must be used. Ifhowever the AGC varies slowly or insignificantly (for example, by lessthan twenty percent) with time then the input standard deviation of theIQ data (σ_(iq)) may be approximated to a constant value and the methodbelow may be used.

Constant Input Variance Method

In scenarios where continuous accumulation is not necessary, analternative is to assume that the input variance is constant and use theformula derived below. This method requires less computational workwhich is an advantage in modern signal receivers where signal processingtakes place in real time, making efficiency critical. Even when constantinput variance is assumed, there is still some resistance to smallchanges in the AGC with time since the quantities measured by thestatistical analyser 7 are proportional to the input standard deviationof the IQ data (σ_(iq)).

First define a factor κ equal to the standard deviation for the searchbin (a) multiplied by the square root of the number of correlation runs(M), divided by: alpha times the mean for the search bin (μ).

$\begin{matrix}{\kappa = \frac{\sigma \sqrt{M}}{\alpha \; \mu}} & (11)\end{matrix}$

Using equations (7) and (4) σ may be found as:

$\begin{matrix}{\sigma = {\alpha \sqrt{\sum\limits_{m = 1}^{M}\mu_{m}^{2}}}} & (12)\end{matrix}$

That is, the standard deviation for the search bin (σ) is equal to alphatimes the square root of the sum over correlation runs labelled m=1 tom=M of each squared correlation run mean (μ² _(m)).

If the input variance (σ_(iq)) is constant then, by equation (5), thecorrelation run mean (μ_(m)) must also be constant. Therefore for allcorrelation runs, the correlation run mean (μ_(m)) is always equal to aconstant value μ₀:

μ_(m)=μ₀   (13)

Thus, equation (12) becomes:

σ=αμ₀√{square root over (M)}  (14)

That is, the standard deviation for the search bin (σ) is equal to alphatimes the constant correlation run mean (μ₀) times the square root of M.

Using the constant correlation run mean in equation (6) the mean for thesearch bin (μ) may be found to be equal to μ₀ times M:

$\begin{matrix}{\mu = {{\sum\limits_{m = 1}^{M}\mu_{0}} = {\mu_{0}M}}} & (15)\end{matrix}$

Substituting equations (14) and (15) into equation (11) gives:

$\begin{matrix}{\kappa = {\frac{\alpha \; \mu_{0}\sqrt{M}\sqrt{M}}{\alpha \; \mu_{0}M} = 1}} & (16)\end{matrix}$

That is, factor kappa is equal to one. By substituting equation (16)back into equation (11) a relationship between the mean and standarddeviation of the NCS integrator output in the search bin as a functionof how far the integration has progressed is obtained:

$\begin{matrix}{\sigma = {\frac{\alpha}{\sqrt{M}}\mu}} & (17)\end{matrix}$

That is, the standard deviation for the search bin (σ) is equal to alphaover the square root of M, all multiplied by the mean for the search bin(μ).

The constant input variance approach can be expected to break down whenthe incoming standard deviation is not constant over the course of theintegration. In that case the implicit assumption made, that the sum ofthe variances is the same as the sum of the mean variance, does nothold.

If the AGC remains reasonably constant throughout the integration thenequation (17) should provide a reasonably good approximation. However,if the AGC varies significantly over the integration, for example bygreater than twenty percent, then it may be more appropriate to use thecontinuous accumulation approach which works for any AGC profile.

FIGS. 3 a and 3 b are provided to illustrate the impact of different AGCprofiles over the course of the integration, up to two thousandcorrelation runs. FIG. 3 a shows plots of some different integration AGCprofiles with input standard deviation (σ_(iq)) plotted over twothousand correlation runs. These are: fifty percent step up fromσ_(iq)=1 at M=1000, fifty percent step down from σ_(iq)=1 at M=1000,linear ramp from σ_(iq)=0.5 with gradient 0.0005 and sinusoid startingat σ_(iq)=1 with amplitude 0.5 and period 1000 correlation runs. FIG. 3b shows the corresponding effect on kappa. It can be seen that thedeviations from kappa equals one do not exceed ten percent over thisintegration length for these AGC profiles; kappa is relativelyinsensitive to the AGC profile.

Example Method for Calculating the Actual Standard Deviation of the NCSIntegrator Output in a Search Bin

The example hardware proposed for performing the claimed method iscapable of automatically measuring both the total current NCS integratoroutput for a search bin (Y) and also the sum of all the absolute valuesof: the NCS integrator output for each sample minus a number that can beset for the search bin (X):

$\begin{matrix}{X = {\sum\limits_{n = 1}^{N}{{S_{n} - S_{0}}}}} & (18) \\{Y = {\sum\limits_{n = 1}^{N}S_{n}}} & (19)\end{matrix}$

That is, the hardware measures NCS integrator output values (S_(n)) andcomputes both X, the sum of the absolute differences between each NCSintegrator output value (S_(n)) and a set value (S₀), and Y, the sum ofall the NCS integrator output values. If the set value (S₀) is set equalto the search bin mean (μ), i.e.

S₀=μ  (20)

then Y is proportional to that mean and X is proportional to the searchbin standard deviation (σ).

The constant of proportionality between X and the standard deviation maybe calculated by assuming a specific distribution for the abs values.The incoming IQ data is largely thermal noise and thus is distributed asa Gaussian. Therefore the output from the matched filter is a complexGaussian. The absolute value of a complex Gaussian distribution is aRayleigh variate but, due to the central limit theorem, the NCSintegrator output returns to a roughly Gaussian distribution. For aGaussian distribution, the standard deviation for the search bin after Mcorrelation runs (σ) is:

$\begin{matrix}{{\sigma \approx \sqrt{\frac{1}{N}{\sum\limits_{n = 1}^{N}\left( {S_{n} - \mu} \right)^{2}}} \approx {\sqrt{\frac{\pi}{2}}\frac{1}{N}{\sum\limits_{n = 1}^{N}{{S_{n} - \mu}}}}} = {\frac{1}{N}\sqrt{\frac{\pi}{2}}X}} & (21)\end{matrix}$

That is, the standard deviation for the search bin (σ) for a Gaussiandistribution is approximated by the square root of the maximumlikelihood estimate for the variance. The maximum likelihood estimatefor the variance of a Gaussian distribution is one over the number ofsamples (N), multiplied by the sum over N samples of the square of: theNCS integrator output for each sample (S_(n)) minus the mean for thesearch bin (μ). This expression is approximately equal to the squareroot of one half pi, divided by N, multiplied by the sum over N samplesof the modulus of: the NCS integrator output for each sample (S_(n))minus the mean for the search bin (μ). Thus, if equation (20) holds, thestandard deviation for the search bin may be calculated by multiplying Xby the square root of one half pi, and dividing by N.

The above method requires an accurate estimation of the mean so thatequation (20) approximately holds. One way of estimating μ is to takethe mean from the previous loop and use it to set the mean for thecurrent loop with a small correction added to account for having addedone further term. The small correction can be re-calculated occasionallyto avoid having to perform a division every loop. For example, S₀ couldbe set equal to the mean for the search bin as calculated after M−1correlation runs, plus a correction factor which is the current estimateof how much the mean changes per correlation run. This correction factorcould be re-calculated on every power of two as the current search binmean shifted by the appropriate amount. Therefore the number ofcomputationally-hungry divisions necessary would be low. Alternatively,S₀ may be set to an estimated mean that is roughly correct and thencorrected after evaluation once the true mean has been observed.

The Ratio of Predicted to Measured Standard Deviation

Recall that if the ratio of predicted to measured standard deviation fora search bin is close to one then the search bin is considered to befree of coherent interferers. In order to calculate this ratio (R), notethat μ appearing in equation (17) is the mean for the search bin afternon-coherent integration of N samples, and is thus equal to Y divided byN:

$\begin{matrix}{\mu = \frac{Y}{N}} & (22)\end{matrix}$

Therefore the ratio of expected (calculated using the constant inputvariance method) to actual standard deviation for the search bin isgiven by equation (17) divided by equation (21), substituting for pusing equation (22) and alpha using equation (3):

$\begin{matrix}\begin{matrix}{R = \frac{\sigma_{theory}}{\sigma_{measured}}} \\{= \frac{\frac{\alpha}{\sqrt{M}}\mu}{\frac{1}{N}\sqrt{\frac{\pi}{2}}X}} \\{= \frac{\frac{\alpha}{\sqrt{M}}\frac{Y}{N}}{\frac{1}{N}\sqrt{\frac{\pi}{2}}X}} \\{= {\alpha \sqrt{\frac{2}{\pi \; M}}\frac{Y}{X}}} \\{= {\frac{\sqrt{2\left( {4 - \pi} \right)}}{\pi}\frac{1}{\sqrt{M}}\frac{Y}{X}}}\end{matrix} & (23)\end{matrix}$

That is, the ratio of predicted to actual standard deviation for thesearch bin is the square root of: two times four minus pi, all dividedby pi, all multiplied by one over the square root of M, all multipliedby the quotient of: the sum of all the absolute sample values in asearch bin (Y) and the sum of the absolute differences between the valueof absolute sample value and the mean for the search bin (X).

Cross-Correlation Rejection

If there is a coherent interferer in a search bin then this will have asubstantial impact on R. This is because the cross-correlationaccumulates linearly just like for the target signal rather than in arandom diffusive manner as noise does. Thus, even for weak interfererswell below the thermal noise floor their size grows proportionally tothe integration time so that after sufficient integration time even avery weak interferer will be above the nominal noise floor. Simplyincreasing the threshold above which a cross-correlation peak isidentified as a candidate code phase offset for the target signal willtherefore only delay the time when a spurious signal will be identified,and additionally de-sensitizes the receiver to the target signal. If thetarget GPS satellite signal is detected then a single cross-correlationpeak will be observed, so the search bin mean will rise, but thestandard deviation will stay approximately constant. However, unlike areal GPS signal coherent interferers impact multiple code phases in asearch bin and thus provide a statistical signature that allowscontaminated bins to be identified by raising the search bin standarddeviation above that expected for a clean signal, and hence lowering Rbelow 1 (the quantity X, proportional to the search bin standarddeviation, is in the denominator of the expression for R in equation(23)).

FIG. 4 shows R plotted against cumulative density for an N=1536 searchbin and AWGN input. The standard error in the measurement of standarddeviation in this case is 1.95%. This scales approximately as theinverse root of the number of samples. Under the null hypothesis (AWGNinput) in this example, R is always within the range 0.9-1.1. Thereforea search bin could be deemed to be suspect if its value of R felloutside of this range.

In practice the predicted and observed standard deviations themselvescould be calculated directly, or their ratio could be calculated inisolation using equation (23). This could be done using, for example, adigital signal processor. In order to improve processing speed andmaintain sufficient efficiency to meet the real time requirement themain operations used in the coding for the calculations could be singlecycle instructions on the digital signal processor such as multiples,bitshifts, additions and msb. For example, in order to avoid carryingout a division to calculate R, Y could be compared with the product of:X and the square root of M (from equation 23 it may be seen that R isproportional to the quotient of these two quantities therefore comparingtheir size is essentially evaluating R).

Accepting only a narrow range of R values results in fewer false alarmswhen candidate code phases of correlation peaks from search bins deemedto be clean are presented to the next stage of the acquisition processfor verification.

Candidate Code Phase Offset Identification

Once a search bin has been classified as clean, candidate code phaseoffsets can be identified from sample values. A candidate code phaseoffset (nΔτ) may be, for example, that of a sample n with a value(s_(n)) which exceeds a certain multiple (φ) of the search bin standarddeviation (σ). Therefore, after M correlation runs the NCS integratoroutput value for an offset divided by M (S_(n)/M) could be compared to athreshold (φσ) which is a multiple (φ) of the search bin standarddeviation (σ). This multiple could be, for example, four. Code phasecandidates could also be obtained at sub-integer values of n byinterpolation.

Identification of candidates therefore requires an estimate of thesearch bin standard deviation (σ). This could be an empirical estimate,calculated from a measurement of X, or a theoretical value for an AWGNinput calculated using equations 2 and 5 (for a clean search bin theyshould be approximately equal). The empirical standard deviationgenerally exceeds the theoretical standard deviation due to the effectsof non-AWGN noise. Therefore using the empirical standard deviationusually provides a higher threshold and consequently the identificationof fewer candidates. The empirical standard deviation could therefore beused when R<1 (ie when coherent interferers are more likely to bepresent) and the theoretical standard deviation could be used when R>1.

Even when using the empirical standard deviation for thresholding,random noise can lead to false alarms being generated for candidateswhich are the result of noise, not a signal from the target satellite.This effect is illustrated in FIGS. 5 a, 5 b and 5 c which show examplecross-correlations after removal of the mean and division by thestandard deviation. FIGS. 5 a-1 and 5 a-2 show a single example (in FIG.5 a-2) compared to Gaussian data (in FIG. 5 a-1). The cross-correlationresults are clearly skewed to higher values than a pure AWGN input wouldgive. FIG. 5 b shows the probability density function (pdf) for tendifferent PN pairs and a reference Gaussian. This shows that in thepresence of cross-correlation the distribution is clustered round zerobut has a long tail. FIG. 5 c shows the corresponding cumulativedistribution function (cdf). The excess visible in the cdf for points inthe region greater than one standard deviation means that there are alot of points positioned above where they would be by chance.

In the presence of strong cross-correlation refinements are possiblethat significantly further reduce the number of false alarms. Someexamples of these refinements will now be described.

Multi-Level Classification

Multiple classification levels could be used for blacklisting,greylisting or whitelisting particular search bins based on the R valuethey exhibit. Search bins exhibiting R values further from one than themost extreme classification boundaries could be automatically rejectedand not used at all; these would form the blacklist. Search binsexhibiting R values closer to one than the least extreme classificationboundaries (for example between 0.9 and 1.1) could be automaticallytrusted; these would form the whitelist. Search bins exhibitingsuspicious values of R outside of the safe whitelist range but not asbad as those which warrant blacklisting would form the greylist.Greylisted search bins could have stricter thresholds imposed on furthercorrelation runs by increasing the value of φ used.

Under this cross-correlation detection scheme below a certain value of R(for example R<0.75) a bin is blacklisted and not used. Search bins withR values unlikely to be produced on a clean search bin by chance aregreylisted and their thresholds raised. Thus they still have a chance torecover but any real signals must become cleaner before they areaccepted.

The threshold modifications applied to the greylisted search bins couldalso be multi-level. For example the threshold modifier φ could beincreased by 1 for search bins with R values between 0.90625 and 0.9375,by 3 for search bins with R values between 0.84375 and 0.90625 and by 4for search bins with R values between 0.75 and 0.84275. φ would beeffectively infinite for the blacklisted search bins with R values below0.75.

Example results including coherent interferers affecting some searchbins are shown in FIGS. 6 a and 6 b. The dashed lines are at R=0.9 andR=1.1 and represent the envelope expected under the null hypothesis. Ris plotted as a function of integration time for 21 frequency bins. Thefrequency bins are spaced at 55.5 Hz with the centre bin on zero andhave a coherent integration time T of 12 ms. In FIG. 6 a the bins arecentred on a −110 dBm (approximately 60 dBHz) cross-correlation. All buttwo frequency bins are quickly distinguishable from the null hypothesis.Those two bins survive and are deemed free of coherent interference andthus safe to use. In FIG. 6 b the bins are centred on a −130 dBm(approximately 40 dBHz) cross-correlation. In this case only 3 binsappear to have coherent interference present.

Variable Classification Boundaries

FIG. 7 shows R as a function of integration time for one hundred searchbins as calculated under the null hypothesis. With no coherentinterference in the search bin R takes a value close to one. It can beseen that there is a slight drift towards negative values over longintegration times. This is due to quantisation noise in the blockfloating point representation used. With values of M around one to twothousand this effect is negligible.

FIG. 8 illustrates that even though there is a slight drift towardsnegative values of R on progressing through the quartiles of theintegration due to block floating point quantisation noise in thealgorithm used, the distribution remains within the 0.9 to 1.1 rangeover the 1000 run process of FIG. 7. Therefore if the integration lengthis limited the quantisation noise should not pose a problem.

However, for longer integration (for example around M=4000), thequantisation error drift could be significant enough to incorrectlytrigger coherent interferer detection. FIG. 9 shows R values as low as0.85 from clean signals after 4000 correlation runs. This situation canbe avoided by modifying the classification boundaries to take thequantisation noise into account. For example, the boundary value of Rused to determine whether a cross-correlation is present or not could,instead of being a flat value such as 0.9 over the whole process, belowered over the course of the integration to compensate for the effectsof quantisation noise. This technique could be applied to all theclassification boundaries if multi-level classification is used.

Threshold Shaping

As can be seen from FIGS. 6 a and 6 b, there is a short period at thestart of the acquisition process where the spurious signals have valuesof R close to one. This could result in spurious signals being acceptedif the thresholds were flat. Therefore it may be preferable to increasethe thresholds by temporarily increasing φ during a grace period nearthe start of the integration, then restore them to their normal levelsso as not to detract from the ultimate sensitivity of the method. Thusthere could be a grace period during which the thresholds are set closerto one standard deviation than normal to avoid incorrect identificationsof cross-correlations as usable signals. This technique slows down theacquisition time slightly but the balance between speed and accuracyshould be suitable for intermediate strength signals.

The grace period could be set so that cross-correlations from satellites20 dBs above the search sensitivity will reveal themselves, that is haveR values less than 0.9 (or some other classification boundary). Thethresholds could, for example, be modified linearly as in FIG. 10, beinghigh at the start of the acquisition process and reducing graduallyuntil reaching their standard levels around half-way through the totalnumber of correlation runs, where the thresholds would level out. Theinitial threshold level and grace period could be configurable butapplicable to all the searches.

Limiting the Number of Candidates

Another precaution to prevent acceptance of spurious cross-correlationscould be to limit the number of candidate code phases allowed per searchbin before that bin is grey or blacklisted. The thresholds may be setsuch that two candidates in a search bin would be unusual, threeimprobable and four practically impossible for a clean signal.Consequently, if the number of candidates from a specific search binexceeds three then that bin could either be rejected or integrationsre-run on it with a higher threshold.

As soon as a candidate is identified and submitted to verification, thecorresponding code phase bin in the acquisition could be set to, forexample, the mean search bin value so that any other candidates becomevisible. If too many other candidates are found then all currentverifications on candidates from that search bin could be aborted asthey cannot be trusted and it is preferable to free resources.

Re-Inspecting R

Once a candidate signal has been identified by the acquisition engine,it could pass the signal details to a verification engine. Whileverification is taking place, the acquisition engine could continue torun integrations on the search bin. The final step in the verificationcould then be to re-check the R value. If this remains within thestrictest classification boundaries then the signal could be locked-onto. If however, the R value has crossed a boundary while verificationhas been running (this could for example be due to a coherent interfererswitching on during that time) then the candidate could be rejected orintegration on that search bin could be re-run with more demandingthresholds which may depend on R or the change in R. R could also bechecked periodically during verification, not just on completion.

Continuous Wave (CW) Interference

This type of coherent interference affects the accumulations differentlyto interference from satellites and all other types of coherentinterference Therefore a separate CW detector could be included. Thisdetector could be implemented before or after signal detection.Alternatively, R can be calculated as before.

For pure tones each point in the cross-correlation has the same absolutevalue and differs only by a phase shift. Therefore in this idealisedcase the impact of the CW interference does not depend on thecross-correlation offset.

This can be seen from the expression for a cross-correlation from a pureCW tone at code offset kΔτ.

$\begin{matrix}\begin{matrix}{{\Gamma_{k}} = {{\sum\limits_{n}{c_{n}^{{- 2}\pi \; \; f\; \Delta \; {t{({n - k})}}}}}}} \\{= {{^{2\pi \; \; f\; \Delta \; {tk}}} \cdot {{\sum\limits_{n}{^{{- 2}\pi \; \; f\; \Delta \; {tn}}c_{n}}}}}} \\{= {{\sum\limits_{n}{^{{- 2}\pi \; \; f\; \Delta \; {tn}}c_{n}}}}}\end{matrix} & (24)\end{matrix}$

That is, the absolute value of the cross-correlation of all the samplesin a search bin with a certain PN and frequency f with a pure CW toneinterference signal at code offset τ (Γ_(τ)) is given as the absolutevalue of the sum over all samples of sample n (c_(n)) multiplied by theexponential of: negative two times pi times i multiplied by thefrequency f of the pure tone, the period of the pure tone (Δt), and nminus τ. The exponential term containing τ may be factored out, and isequal to one, leaving an expression for the cross-correlation which doesnot contain τ.

Equation 24 illustrates both that the absolute value of thecross-correlation for a pure tone is independent of code phase and alsothat this value is the absolute value of a sum of all the samples fromthe matched filter each multiplied by a different phase shift. This canbe thought of as equivalent to the length of the resultant from a sum of2D vectors each of which is rotated. At each different frequency thephasing will differ leading the vectors to sum more or lessconstructively leading to different offsets to the mean absolute valueof the cross-correlation.

Consequently, the mean for a search bin containing a pure CWinterference tone is elevated, but the standard deviation is not changedas the interference affects all code phases equally. Recall that inequation (23) for R, Y (which is proportional to the mean) is in thenumerator, and X (which is proportional to the standard deviation) is inthe denominator. Thus CW interference tends to push R above one(assuming the CW signal may be approximated to a pure tone).

In reality, because the PN repeats cyclically periodic boundaryconditions are imposed on the cross-correlation, and when the frequencyis not consistent with these boundary conditions low frequency structureis introduced into the cross-correlation as a function of code phase.This structure causes R to behave in a similar way as forinter-satellite cross-correlation; that is it drops from high to lowover the course of acquisition. In practice narrowband interferers oftencome close to being ideal, so R will start off with a high value, oftenmuch higher than one, but will then reduce over time so that if thenumber of integrations is high it can drop below one. Therefore for CWinterferers it may be useful to use the technique discussed previouslyof thresholding with the empirical standard deviation when R<1 and thetheoretical standard deviation when R>1. With this mechanism in placefalse alarms due to pure CW interferers should be rare.

For example, FIG. 11 shows R plotted for several search bins over thecourse of acquisition for a pure tone CW interferer at 5 kHz and 60dBHz. The highest line is for the 0 Hz search bin, the middle pair oflines are for ±10 Hz search bins, and the lowest pair of lines are forthe ±20 Hz search bins.

While it is mainly the shape of the lower threshold which matters forsatellite-based interference, it can be seen from FIG. 11 that for CWinterference it is the upper threshold which could be varied over thecourse of the integration to ensure that search bins containing CWinterference are not locked-on to.

CW rejection could be enhanced by comparing R after m correlation runsto the initial value of R, instead of comparing empirical andtheoretical values of R. If there is no CW then the ratio of the currentto the initial value of R is almost the same as the current value of R.If there is a CW then the ratio will rapidly drop off and could berejected using a similar mechanism to that discussed previously.

Instead of performing acquisition on all signals then ignoringcontaminated search bins, CW interferers could be identified and excisedfrom the fast Fourier transform during acquisition.

Alternative Techniques for Avoiding the Capture of CW Interference

FIG. 12 shows a schematic diagram of how code phase offset candidatesfor a particular frequency and PN search bin may typically be passedfrom an acquisition engine to a verification engine and the correctoffset verified and used for locking-on to a target satellite. As hasbeen discussed previously (and as illustrated in FIG. 2) NCS integrator6 maintains a table of the abs values (s_(m,n)) for each sample (n) aseach correlation run (m) is performed. This may be stored in an NCSmemory 6 a. The abs values may then be passed to a peak sort section 6 bwhere they are ordered by magnitude. The resulting ordered list may bekept in a peak store 6 c. The list may then be passed to a top L selectalgorithm 6 d which selects the highest L abs values (s_(m,n)) in theordered list, where L is a predetermined integer. These are then passedto an L verify track channels section 9 for verification. In themeantime the next correlation run (m+1) takes place and its resultsundergo the same processing so that another list of L peaks are passedto the L verify track channels section 9. There the two lists undergonon-coherent summation such that abs values s_(m+1,n) are added tocorresponding abs values s_(m,n) forming a third ordered list comprisinggreater than or equal to L values. This third list is passed back to thetop L select algorithm 6 d which selects the top L values and updatesthe list held in the L verify track channels section 9. This process isrepeated until the list has been updated using the final (M^(th))correlation run results. The final list will therefore only containsamples corresponding to code phases which have produced consistentlyhigh results. It can then be passed to signal lock detect section 10 sothat the receiver can lock-on to the target satellite. This process isrepeated for different search bins until enough satellites have beenlocked-on to for the receiver to be able to calculate its position.

The process of FIG. 12 will usually result in successful lock-on in thepresence of Gaussian noise. However if CW interference is present thenall L of the top peaks could be due to the interference. Therefore itwould be advantageous to be able to detect search bins contaminated withCW interference. This could be done at any stage. A schematic diagramfor an example system which includes a CW detector is shown in FIG. 13.It includes a CW detector 11 between the NCS memory 6 a and the peaksort section 6 b which does not allow a CW to be declared as one of thetop L NCS peaks.

FIG. 14 shows how a CW detector may function when used before signaldetection. The abs values for the current correlation run (m) are allsummed, giving a value Q_(m). The CW detector outputCW_(indicator noise) is formed by comparing Q_(m) and a noise channel(n_(channel)) via a threshold (T_(R)). A noise channel is typicallyformed in such a way as to avoid significant variations in its outputdue to small numbers of narrowband interferers. This is done by forminga noise channel as the sum of numerous and various (NCS summations) codephase and carrier frequency offsets such that any individual narrowbandinterferer will have limited impact on the noise channel output value.Therefore the noise channel does not vary with the introduction of anarrowband interferer but an acquisition channel (consisting of a singlecarrier frequency and multiple code phase hypotheses) will varysubstantially if the narrowband interferer and the signal carrierfrequency are close. Additionally a noise channel typically integratesnoise power that varies with variation of AGC in the receiver. This isbecause the noise power is updated at regular intervals (e.g. every 20msecs) and will vary with changing AGC across longer periods (e.g. 1second). As long as the algorithm comparing Q_(m) and noise channel isperformed across substantially similar time periods they will be subjectto the same AGC variation but their ratio will not.

FIG. 15 shows a typical output from the detector for a CW=5 dB-Hz. FIG.16 shows the probability of CW detect performance for different carrierto noise ratios (CNOs). (PDI=pre-detection integral, i.e. coherentintegration period, NCS=non-coherent summation time, Pfa=probability offalse alarm.)

FIG. 17 shows how a CW detector may function when used after signaldetection. In this case the signal detect hypothesis code phase has n=z.A value D is calculated by subtracting from Q_(m) the sum of the absvalues for the samples surrounding sample z. D is then compared withsample z via a threshold T_(S).

FIG. 18 is a flow diagram of the detection process when both CWdetections are performed. If a CW is not detected before signaldetection then there is none present and the process can proceed asnormal. If a CW is detected it is possible that it is a weak CW andthere may also be a strong target signal present. Therefore a check ismade for signal peaks. If no signal is detected then the search bin isrejected due to CW interference. If a signal is detected then the secondkind of CW detection is performed. If this gives a true output then thesearch bin is rejected again, but if it gives a false output then asignal is detected.

Conclusions

A non-parametric method of detecting coherent interferers is preferablesince it provides protection from unexpected and previously undetectedinterferers. It can avoid the requirement to obtain an adequateparametric representation of the cross-correlations they produce. Thiscan mean that parametric data regarding newly launched satellites neednot be collected. For example it might not be necessary to track Japan'snew QZSS (Quasi-Zenith Satellite System) constellation of satellites.Additionally, signals may be suppressed before they are detected,reducing the processing load required to reject them.

Various modifications could be made to the method, for example thestatistics measured need not be the mean and standard deviation of allthe samples in the search bins. They could be any reasonable surrogates,including, for example, percentiles or the count of samples above orbelow some settable threshold. The NCS integrator could accumulate thesquares of the sample values rather than their absolute values. Thestatistical signature determined may be calculated using any number ofprocessing steps.

The method could be used in conjunction with traditional parametrictechniques to improve accuracy.

Implementation does not necessarily have to be in GPS or a GNSS at all;other positioning systems, including terrestrial systems, could benefitfrom using the method disclosed. In fact it could be used in any systemwhich needs to detect coherent interference for the purpose oflocking-on to a specific signal to the exclusion of coherentinterference signals, or for any other reason.

The applicant hereby discloses in isolation each individual featuredescribed herein and any combination of two or more such features, tothe extent that such features or combinations are capable of beingcarried out based on the present specification as a whole in the fightof the common general knowledge of a person skilled in the art,irrespective of whether such features or combinations of features solveany problems disclosed herein, and without limitation to the scope ofthe claims. The applicant indicates that aspects of the presentinvention may consist of any such individual feature or combination offeatures. In view of the foregoing description it will be evident to aperson skilled in the art that various modifications may be made withinthe scope of the invention.

1. A method for detecting coherent interference, the method comprisingthe steps of: (a) receiving a signal at a first frequency, (b) forming aset of cross-correlation results by at least cross-correlating thesignal with a first known code for a plurality of code offsets, (c)determining a first statistical signature of the cross-correlationresults, and (d) deciding, based on the first statistical signature,whether non-negligible coherent interference is present within a firstsearch bin defined by the combination of the first frequency and thefirst known code.
 2. A method as claimed in claim 1, wherein the saidstep (b) of forming the set of cross-correlation results comprises atleast: (b.i) cross-correlating the signal with a first known code for aplurality of code offsets, and (b.ii) either: (b.ii.1) determining theabsolute values of the values output by the said step (b.i), or (b.ii.2)determining the squares of the values output by the said step (b.i). 3.A method as claimed in claim 2, wherein the said step (b) of forming theset of cross-correlation results further comprises at least: (b.iii)non-coherently summing all the values determined in the said step (b.ii)each time a new value is determined by the said step (b.ii).
 4. A methodas claimed in claim 1, wherein the first known code is a pseudorandomcode.
 5. A method as claimed in claim 1 wherein the first statisticalsignature indicates the extent to which peak values among thecross-correlation results are clustered about plural code offsets.
 6. Amethod as claimed in claim 2, comprising the step of: comparing thevalues determined in the said step (b.ii.1) to a threshold value, anddetermining any code offsets having cross-correlation results higherthan that threshold value to be candidates for future receivingoperations.
 7. A method as claimed in claim 6, comprising the step ofcalculating the threshold value as the mean of the values determined inthe said step (b.ii.1) plus a multiple of their standard deviation.
 8. Amethod as claimed in claim 6, comprising re-calculating the thresholdvalue from time to time.
 9. A method as claimed in claim 6, wherein themethod comprises assessing whether the number of candidates exceeds apredetermined value, and if it does determining there to benon-negligible coherent interference present in the first search bin.10. A method as claimed claim 1, wherein the said step (d) comprisescomparing the first statistical signature with a first expectedstatistical signature.
 11. A method as claimed in claim 10, wherein thefirst expected statistical signature is the statistical signatureexpected in the absence of non-negligible coherent interference.
 12. Amethod as claimed in claim 10, wherein the first expected statisticalsignature is that of additive white Gaussian noise.
 13. A method asclaimed in claim 10, wherein the coherent interference is continuouswave interference and the first expected statistical signature is thatof a noise channel.
 14. A method as claimed in claim 1, wherein the saidstep (d) comprises treating the received signal as having a constantvariance.
 15. A method as claimed in claim 10, comprising assessingwhether the first expected and measured statistical signatures deviateby less than a first predetermined amount and if they do thendetermining that there is negligible coherent interference in the firstsearch bin.
 16. A method as claimed in claim 10, comprising assessingwhether the first expected and measured statistical signatures deviateby more than a second predetermined amount and if they do thendetermining that there is non-negligible coherent interference presentin the first search bin.
 17. A method as claimed in claim 16, whereinthe method comprises assessing whether the first expected and measuredstatistical signatures deviate by an amount between a firstpredetermined amount and said second predetermined amount, and if theydo then in the said step (d) repeating the previous steps with anincreased threshold value.
 18. A method as claimed in claim 17,comprising re-calculating the first and/or second amounts from time totime.
 19. A method as claimed in claim 1, wherein the first statisticalsignature is the standard deviation of the cross-correlation results.20. A method as claimed in claim 1, wherein the method is performed fora plurality of search bins.
 21. A method as claimed in claim 1, whereinthe known code is a code transmitted by a particular remote transmitter.22. A method as claimed in claim 1, wherein the method is performed aspart of a signal acquisition process.
 23. A method as claimed in claim1, wherein the received signal originates from a satellite.
 24. A methodas claimed in claim 13, further comprising the steps of: if the saidstep (d) resulted in a decision that coherent interference was presentwithin the first search bin then (e.i) repeating the previous steps fora second search bin, different to the first search bin; or if the saidstep (d) resulted in a decision that coherent interference was notpresent within the first search bin then (e.ii) determining a secondstatistical signature of the cross-correlation results, and deciding,based on the second statistical signature, whether non-negligiblecoherent interference is present within the first search bin.
 25. Asystem for detecting coherent interference, the system comprising: asignal receiver for receiving a signal at a first frequency, across-correlator for forming a set of cross-correlation results by atleast cross-correlating the received signal with a first known code fora plurality of code offsets, a processor for determining a firststatistical signature of the cross-correlation results, and a decisionunit for deciding whether non-negligible coherent interference ispresent within a first search bin defined by the combination of theparticular frequency and the first known code based on the statisticalsignature.